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Real-time one-step phase locking with physics-informed phase estimator in multichannel coherent beam combining 认领 引用
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作者 Yong Wu Guoqing Pu +3 位作者 Jiajin Wang Zhiwei Fang Weisheng Hu Lilin Yi 《Advanced Photonics Nexus》 CSCD 2026年第3期214-221,共8页
Coherent beam combining(CBC)is an effective approach to surpass the power limitations of single fiber lasers,where precise phase control is essential.As the demand for higher power and more channels increases,achievin... Coherent beam combining(CBC)is an effective approach to surpass the power limitations of single fiber lasers,where precise phase control is essential.As the demand for higher power and more channels increases,achieving faster phase control becomes increasingly challenging for traditional methods.We propose a scheme termed physics-informed phase estimator(PIPE),which can be considered as a graybox model merging locking of optical coherence by single-detector electronic-frequency tagging(LOCSET)and a well-trained neural network,that infers the phase differences between channels from the temporal intensity of the combined beam after assigning unique frequency tags to each sub-beam.Real-time onestep phase locking with a single photodetector is experimentally demonstrated in a four-channel CBC system by incorporating PIPE,achieving nearly an order-of-magnitude improvement in convergence time over LOCSET,with a residual phase ofλ∕45 and a combining efficiency of 98%.The results indicate that PIPE offers a promising solution for substantially enhancing phase control bandwidth in future CBC systems,especially for filled-aperture CBC systems. 展开更多
关键词 coherent beam combining phase locking physics-informed phase estimator neural network
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Two-Sample Order Statistics Estimator for SNR Estimation of Meteor Burst Communication Signals 认领 引用
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作者 Zhou Yanping Gao Rui 《China Communications》 SCIE EI CSCD 2026年第5期197-204,共8页
Meteor burst communication exploits transient meteor trails to enable beyond-line-of-sight transmission,where the inherently short-lived channel conditions impose stringent requirements on rapid and reliable signal-to... Meteor burst communication exploits transient meteor trails to enable beyond-line-of-sight transmission,where the inherently short-lived channel conditions impose stringent requirements on rapid and reliable signal-to-noise ratio estimation.This study proposes a novel pilotless SNR estimator founded on the two-sample order statistics method,which quantifies the distributional deviation between received signal amplitudes and pre-generated reference distributions without assuming a specific noise model.Comprehensive Monte Carlo simulations under BPSK,QPSK,and 16-QAM modulations in additive white Gaussian noise environments demonstrate that the proposed OS-based approach consistently surpasses the M2M4,M8,and Kolmogorov–Smirnov estimators in terms of estimation accuracy,normalized mean squared error,and success rate,with particularly notable gains in limited-sample scenarios.These results underscore the method’s robustness and adaptability for SNR estimation in MBC applications. 展开更多
关键词 meteor burst communication non-dataaided order statistics SNR estimation
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Continuous Bayesian probability estimator in predictions of nuclear charge radii 认领 引用 被引量：2
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作者 Jian Liu Kai-Zhong Tan +4 位作者 Lei Wang Wan-Qing Gao Tian-Shuai Shang Jian Li Chang Xu 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第11期283-293,共11页
Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator ... Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator and Bayesian model averaging(BMA)to optimize the predictions of RCfrom sophisticated theoretical models.The CBP estimator treats the residual between the theoretical and experimental values of RCas a continuous variable and derives its posterior probability density function(PDF)from Bayesian theory.The BMA method assigns weights to models based on their predictive performance for benchmark nuclei,thereby accounting for the unique strengths of each model.In global optimization,the CBP estimator improved the predictive accuracy of the three theoretical models by approximately 60%.The extrapolation analyses consistently achieved an improvement rate of approximately 45%,demonstrating the robustness of the CBP estimator.Furthermore,the combination of the CBP and BMA methods reduces the standard deviation to below 0.02 fm,effectively reproducing the pronounced shell effects on RCof the Ca and Sr isotope chains.The studies in this paper propose an efficient method to accurately describe RCof unknown nuclei,with potential applications in research on other nuclear properties. 展开更多
关键词 Machine learning Nuclear charge radii Continuous Bayesian probability estimator Bayesian model averaging
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Distributed predefined-time estimator-based affine formation target-enclosing maneuver control for cooperative underactuated quadrotor UAVs with fault-tolerant capabilities 认领 引用 被引量：2
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作者 Yang XU Yuanfang QU +2 位作者 Delin LUO Haibin DUAN Zhengyu GUO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第1期471-490,共20页
The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aer... The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aerial Vehicles(QUAVs).This scheme mainly consists of predefinedtime estimators and fixed-time tracking controllers,with a hybrid Laplacian matrix describing the communication among these QUAVs.At the first layer,we devise predefined time estimators for leading and following QUAVs,enabling accurate estimation of desired information.In the second layer,we initially devise a fixed-time hybrid observer to estimate unknown disturbances and actuator faults.Fixedtime translational tracking controllers are then proposed,and the intermediary control input from these controllers is used to extract the desired attitude and angular velocities for the fixed-time rotational tracking controllers.We employ an exact tracking differentiator to handle variables that are challenging to differentiate directly.The paper includes a demonstration of the control system stability through mathematical proof,as well as the presentation of simulation results and comparative simulations. 展开更多
关键词 Affine formation maneuver control Target tracking Fixed-time control Quadrotor unmanned aerial vehicle Target enclosing Predefined-time estimation
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Cytokeratin 18 fragment is associated with steatosis-associated fibrosis estimator score and lipid in patients with steatotic liver disease 认领 引用 被引量：1
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作者 Tatsuki Ichikawa Satoshi Miuma +10 位作者 Mio Yamashima Shinobu Yamamichi Makiko Koike Yusuke Nakano Hiruyuki Yajima Osamu Miyazaki Tomonari Ikeda Takauma Okamura Naohiro Komatsu Miruki Yoshino Hisamitsu Miyaaki 《World Journal of Hepatology》 2025年第11期181-199,共19页
BACKGROUND Serum cytokeratin 18 fragment(CK18F)has been developed as a new noninvasive test(NIT)for risk assessment of steatotic liver disease(SLD);however,there are few reports on its relationship with existing NITs ... BACKGROUND Serum cytokeratin 18 fragment(CK18F)has been developed as a new noninvasive test(NIT)for risk assessment of steatotic liver disease(SLD);however,there are few reports on its relationship with existing NITs and association with cardiometabolic risk factors(CMRFs).AIM To clarify the relationship among CK18F,NITs,and CMRF.METHODS We included 125 patients who were assessed for SLD and had CK18F measured in cross-sectional study.The fibrosis-4 index(FIB-4),steatosis-associated fibrosis estimator(SAFE)score,liver stiffness(LS),controlled attenuation parameter,and FibroScan-aspartate aminotransferase(FAST)score were compared with CK18F as existing NITs.RESULTS CK18F was associated with aspartate aminotransferase,alanine aminotransferase,and triglyceride(TG).FAST and SAFE score were associated with high CK18F(>260 U/L),but not FIB-4 or LS.The cut-off values for TG and high-density lipoprotein(HDL)cholesterol used to determine high CK18F using receiver operating characteristics analysis were 126 mg/dL and 56 mg/dL respectively.High TG(>126 mg/dL)and low HDL(126 mg/mL and HDL<56 mg/dL,modified CMRF(mCMRF)was associated with CK18F levels,with a higher risk of high CK18F than CMRF.CONCLUSION CK18F is a new NIT associated with SAFE score and FAST.High TG,low HDL,and mCMRF are associated with high CK18F. 展开更多
关键词 Cytokeratin 18 fragment Steatosis-associated fibrosis estimator score Triglyceride High-density lipoprotein cholesterol Steatotic liver disease
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A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation 认领 引用 被引量：1
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作者 Hamza Murad Khan Anwar Khan +3 位作者 Santos Gracia Villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani 《Computers, Materials & Continua》 SCIE EI 2025年第5期3369-3388,共20页
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models... Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes. 展开更多
关键词 Short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LSTM hyperparameter tuning hybrid prediction model
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Estimation of truck mass and center of gravity using data-driven mechanics:a comparative study of TSO-SVM,CS-BP,SSA-ELM,and WOA-XGBoost in automotive dynamics 认领 引用
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作者 Wei Liu Yuanyou Fu +2 位作者 Yongjun Pan Weicheng Huang Aki Mikkola 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期160-177,共18页
Accurate estimation of a truck’s mass and center of gravity(CG)is critical for optimizing safety and performance butremains challenging due to dynamic uncertainties in weight distribution and road interactions.This s... Accurate estimation of a truck’s mass and center of gravity(CG)is critical for optimizing safety and performance butremains challenging due to dynamic uncertainties in weight distribution and road interactions.This study introduces a datadriven mechanics framework integrating four hybrid machine learning(ML)models-tuna search-optimized support vector machine,cuckoo search-optimized BP neural networks,sparrow search algorithm-optimized extreme learning machine,and whalesearch-optimized XGBoost-to enable estimation.A 17-degree-of-freedom multibody dynamics model,incorporating suspension kinematics via a semirecursive formulation,generates simulation datasets linking real-time tuck states(pitch,roll)to massand CG.Search algorithms leverage physics-derived truck state data to initialize ML hyperparameters,enhancing training efficiency.Validation against multibody benchmarks confirms accuracy,while robustness is demonstrated across driving scenariosand noise.By unifying data-driven ML with physics-based mechanics,this approach advances parameter estimation,bridgingtruck dynamics with computational intelligence for automotive design. 展开更多
关键词 Parameter estimation Data-driven mechanics Multibody dynamics Center of gravity Hybrid machine learning Real-time estimation
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Fast Anomaly Detection and Joint State Estimation for Perturbed Nonlinear Systems With Prolonged Output Anomalies 认领 引用
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作者 Guangdeng Chen Xiao-Jie Peng +2 位作者 Yan Lei Chao Huang Hongyi Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1301-1313,共13页
State estimation under anomalies such as disturbances and faults remains a fundamental challenge in nonlinear systems,with its difficulty further exacerbated by potential network attacks.This study investigates fast a... State estimation under anomalies such as disturbances and faults remains a fundamental challenge in nonlinear systems,with its difficulty further exacerbated by potential network attacks.This study investigates fast anomaly detection and state estimation for perturbed nonlinear systems where actual outputs may be anomalous over a prolonged period.First,a fixedtime observer is constructed.By leveraging integral-type composite Lyapunov functions and homogeneity theory,the error bounds are proven under varying scenarios involving model disturbances,measurement noise,and nonlinearity.Based on these bounds,a fast anomaly detection mechanism is designed.Next,a cascade predictor is developed based on the fixed-time observer,which uses historical outputs from a previous time window to predict the current system state.Simultaneously,an algorithm is proposed to determine the reference historical output based on anomaly detection results,improving long-term prediction accuracy and mitigating the impact of anomaly detection delays.Finally,the secure state estimation is derived by fusing states from the fixed-time observer and the cascade predictor,depending on the anomaly detection results.The effectiveness of the proposed method is demonstrated through simulations on autonomous vehicles. 展开更多
关键词 Anomaly detection cascade predictor fixed-time observer output anomalies secure state estimation
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Unified physics-informed subspace identification and transformer learning for lithium-ion battery state-of-health estimation 认领 引用
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作者 Yong Li Hao Wang +3 位作者 Chenyang Wang Liye Wang Chenglin Liao Lifang Wang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第1期350-369,I0009,共20页
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ... The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO4and public LiCoO2datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance. 展开更多
关键词 Lithium-ion battery Transformer learning Physics-informed modeling Subspace identification State-of-health estimation
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SRKSE:Off-Grid Sub-Nyquist Channel Parameter Estimation for Signals of Opportunity 认领 引用
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作者 Bian Zhiang Lu Hu +4 位作者 Wang Zhisen Li Hao He Xin Chen Jinyu Xiao Jin 《China Communications》 SCIE EI CSCD 2026年第2期1-19,共19页
In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant chal... In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant challenges in real-time processing,especially under sub-Nyquist sampling conditions,due to high data acquisition rates and offgrid errors.To address this,this paper proposes the signal reconstruction and kernel sparse encoding(SRKSE)model,a novel general framework for high-precision parameter estimation.By combining compressed sensing with a deep unfolding network,the SRKSE model not only achieves robust signal reconstruction but also effectively reduces quantization errors.Key innovations of SRKSE include dual crossattention mechanisms for enhanced feature extraction,sinc sparse kernel encoding to minimize quantization errors,and a custom loss function for balanced optimization.With these advancements,SRKSE achieves up to a 650-fold improvement in time of arrival(TOA)estimation accuracy while operating at just 1%of the Nyquist sampling rate.The SRKSE surpasses both conventional and deep learning-based techniques in accuracy and efficiency,especially when operating under sub-Nyquist sampling conditions.Simulations and real-world experiments confirm the reliability and potential of SRKSE for real-time applications in IoT and wireless communication. 展开更多
关键词 channel estimation compressed sensing deep learning DUN off-grid sub-Nyquist TOA
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GENERALIZING SUBDIFFUSIVE BLACK-SCHOLES MODEL BY VARIABLE EXPONENT:MODEL TRANSFORMATION AND NUMERICAL APPROXIMATION 认领 引用
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作者 Meihui ZHANG Yaxue LIU +2 位作者 Mengmeng LIU Wenlin QIU Xiangcheng ZHENG 《Acta Mathematica Scientia》 SCIE CSCD 2026年第2期993-1010,共18页
This work generalizes the subdiffusive Black-Scholes model by introducing the variable exponent in order to provide adequate descriptions for the option pricing,where the variable exponent may account for the variatio... This work generalizes the subdiffusive Black-Scholes model by introducing the variable exponent in order to provide adequate descriptions for the option pricing,where the variable exponent may account for the variation of the memory property.In addition to standard nonlinear-to-linear transformation,we apply a further spatial-temporal transformation to convert the model to a more tractable form in order to circumvent the difficulties caused by the"non-positive,non-monotonic"variable-exponent memory kernel.An interesting phenomenon is that the spatial transformation not only eliminates the advection term but naturally turns the original noncoercive spatial operator into a coercive one due to the specific structure of the Black-Scholes model,which thus avoids imposing constraints on coefficients.Then we perform numerical analysis for both the semi-discrete and fully discrete schemes to support numerical simulation.Numerical experiments are carried out to substantiate the theoretical results. 展开更多
关键词 Black-Scholes model subdiffusion variable exponent error estimate option pricing
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Identification of the optimal phenological periods for summer maize yield prediction using UAV-based multispectral data 认领 引用
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作者 Qin Dai Hong Chen +4 位作者 Ziqiang Chen Chang Liu Gaoliang Li Yakun Wang Xiaotao Hu 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第6期2396-2413,共18页
Timely and accurate forecasting of crop yields is critical for food management and trade.However,only limited research has explored the impact of integrating crop phenotypic parameters(CPPs)with unmanned aerial vehicl... Timely and accurate forecasting of crop yields is critical for food management and trade.However,only limited research has explored the impact of integrating crop phenotypic parameters(CPPs)with unmanned aerial vehicle(UAV)data across different phenological stages on maize yield prediction.The extent to which multi-temporal data enhances the accuracy and reliability of yield projections compared to mono-temporal data has yet to be systematically investigated.To attain the proper balance between accuracy and cost in crop yield estimation,this study proposed a structured framework for identifying the optimal phenological periods for summer maize yield prediction using UAV-based multispectral data.Three classical methods of custom mean decrease accuracy(C-MDA),optimal parameters-based geographical detector(OPGD),and grey relational analysis(GRA)were first used to sort and screen both the CPPs and vegetation indices(VIs)derived from UAV-based information over six growth stages.Ridge regression models based on multi-temporal data combinations and mono-temporal data were established separately,and their performance in yield prediction were compared to identify the optimal phenological stages and the corresponding key factors.Our results showed that C-MDA was much better at factor screening and ranking compared to OPGD and GRA.The green normalized difference vegetation index(GNDVI),normalized difference vegetation index(NDVI),and normalized difference red edge index(NDRE)emerged as the topperforming VIs,while the leaf area index(LAI)and above ground biomass(AGB)proved to be the most effective CPPs.When predicting yield using only mono-temporal data,the dough stage delivered the highest predictive accuracy(R2=0.871,RMSE=0.407 t ha-1),while the tasseling stage was the earliest that achieved yield estimates with acceptable precision(R2=0.810,RMSE=0.493 t ha-1).In contrast,the integration of UAV data from different crop growth stages markedly enhanced the accuracy of yield estimation.Combinations of data from the tasseling,silking,and dough stages were recommended as the best option(R2=0.942,RMSE=0.291 t ha-1).These findings indicate that the precise estimation of maize yields in smallholder fields may be attainable,and present both substantial theoretical insights and practical benefits for the advancement of precision agriculture. 展开更多
关键词 UAV yield estimation multispectral data geographical detector
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Risk assessment and environmental influencing factors of urinary polycyclic aromatic hydrocarbons metabolites in pregnant women in Zunyi,China 认领 引用
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作者 Jing Yang Jing Jiang +9 位作者 Linfei Wu Pei Xu Nian Wu Rui Yu Yanling Xiao Xingting Zheng Rong Zeng Yuanzhong Zhou Yan Xie Xubo Shen 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第1期617-623,共7页
Despite the widespread presence and frequent detection of polycyclic aromatic hydrocarbons(PAHs)in various aspects of life,there is limited research on their exposure levels in pregnant women and cumulative exposure f... Despite the widespread presence and frequent detection of polycyclic aromatic hydrocarbons(PAHs)in various aspects of life,there is limited research on their exposure levels in pregnant women and cumulative exposure from the living environment.This study included 1311 women in late pregnancy from the Zunyi birth cohort and measured the urinary concentrations of 10 hydroxylated PAH metabolites(OH-PAHs).Risk assessment was conducted based on the estimated daily intake to calculate the hazard quotient and hazard index(HI).A linear regression model was used to analyze the relationship between creatinine-adjusted OH-PAHs concentrations and living environment and lifestyle factors,while principal component analysis was applied to trace the sources of PAHs exposure.1-OHPYR was detected in all participants’urine,with naphthalene metabolites having the highest concentrations among creatinine-adjusted PAHs.OH-PAHs concentrations were associated with housing type,room number,cooking frequency,household size,exercise frequency,fuel type,distance from main road,and drinking water source.Pregnant women using traditional fuels and living in bungalows had higher health risks than those using clean energy and living in buildings.Those living within 100 m of a main road had higher HI than those farther away.Coal combustion was identified as the primary source of PAHs exposure.The study emphasizes the importance of reducing PAHs exposure,especially for pregnant women living in polluted environments.It recommends public health interventions such as improving indoor ventilation and providing clean energy to reduce related health risks. 展开更多
关键词 Polycyclic aromatic hydrocarbons Pregnant women Environmental factor Daily intake estimation Risk assessment
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Dynamic parameters prediction of the spatial deployable mechanism:a hybrid approach combining physics-based and data-driven models 认领 引用
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作者 Yanhe Tao Qintao Guo +4 位作者 Jin Zhou Cheng Yi You Zhang Xiaofei Liu Ruiqi Chen 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期535-549,共15页
Accurate prediction of the spatial mechanism's dynamic parameters in microgravity deployment simulations is crucial for identifying potential faults and ensuring precise gravitational compensation.Traditional engi... Accurate prediction of the spatial mechanism's dynamic parameters in microgravity deployment simulations is crucial for identifying potential faults and ensuring precise gravitational compensation.Traditional engineering models are often inaccurate,primarily because of insufficient experimental data and incomplete understanding of physical phenomena,which impedes model bias reduction in information-poor scenarios.We present a novel hybrid approach aimed at improving the predictive accuracy of the dynamic behavior of spatial deployable mechanisms.The graph convolutional network-temporal convolutional network(GCN-TCN)model,a type of deep learning architecture,is utilized for its expertise in forecasting spatio-temporal data through multi-step predictions.Next,the adaptive bandwidth kernel density estimation technique is applied to estimate the probability density function of residuals from the testing set of the GCN-TCN,quantifying predictive uncertainty.The predictive information is further refined using Bayesian inference,integrating a priori knowledge from physics-based models with data from data-driven models to yield robust posterior predictions.The proposed methodology is validated and shown to be robust through rigorous numerical simulations and experimental validation,demonstrating its ability to provide accurate and reliable predictions for the deployment of spatial mechanisms. 展开更多
关键词 Deep learning Kernel density estimation Data fusion Spatial deployable mechanisms Uncertainty quantification Hybrid approach Bayesian
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Mixed-state Berry curvature in quantum multiparameter estimations 认领 引用
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作者 Xiaoguang Wang Xiao-Ming Lu +2 位作者 Yunbo Zhang Libin Fu Shu Chen 《Communications in Theoretical Physics》 SCIE CAS CSCD 2026年第5期80-84,共5页
For pure states,the quantum Berry curvature has been well studied.However,the quantum curvature for mixed states has received less attention.From the concept of symmetric logarithmic derivative,we introduce a mixed-st... For pure states,the quantum Berry curvature has been well studied.However,the quantum curvature for mixed states has received less attention.From the concept of symmetric logarithmic derivative,we introduce a mixed-state quantum curvature and find that it plays a key role in the field of multi-parameter precision estimations.Through spectral decomposition,we derive the mixed-state Berry curvature for both the full-rank and non-full-rank density matrices.As an example,we obtain the exact expression of the Berry curvature for an arbitrary qubit state. 展开更多
关键词 Berry curvature Uhlmann curvature mixed states quantum parameter estimation
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UNO:Unified Self-Supervised Monocular Odometry for Platform-Agnostic Deployment 认领 引用
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作者 Wentao Zhao Yihe Niu +5 位作者 Yanbo Wang Tianchen Deng Shenghai Yuan Zhenli Wang Rui Guo Jingchuan Wang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期205-222,共18页
This work presents UNO,a unified monocular visual odometry framework that enables robust and adaptable pose estimation across diverse environments,platforms and motion patterns.Unlike traditional methods that rely on ... This work presents UNO,a unified monocular visual odometry framework that enables robust and adaptable pose estimation across diverse environments,platforms and motion patterns.Unlike traditional methods that rely on deploymentspecific tuning or predefined motion priors,our approach generalises effectively across a wide range of real-world scenarios,including autonomous vehicles,aerial drones,mobile robots and handheld devices.To this end,we introduce a mixture-of-experts strategy for local state estimation,with several specialised decoders that each handle a distinct class of ego-motion patterns.Moreover,we introduce a fully differentiable Gumbel-softmax module that constructs a robust interframe correlation graph,selects the optimal expert decoder and prunes erroneous estimates.These cues are then fed into a unified back-end that combines pretrained scale-independent depth priors with a lightweight bundling adjustment to enforce geometric consistency.We extensively evaluate our method on three major benchmark datasets:KITTI(outdoor/autonomous driving),EuRoC-MAV(indoor/aerial drones)and TUM-RGBD(indoor/handheld),demonstrating state-of-theart performance. 展开更多
关键词 computer vision pose estimation robotics unsupervised learning
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Design of Composite Two-Channel Disturbance Estimation Adaptive Controller for Backlash-Like Hysteretic Nonlinear Systems 认领 引用
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作者 LIU Qunpo LI Jiakun +2 位作者 FEI Shumin BU Xuhui NAOHIKO Hanajima 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第1期106-116,共11页
For nonlinear systems with backlash-like hysteresis characteristics and external disturbance,a composite two-channel disturbance estimation adaptive controller is proposed to improve the trajectory tracking accuracy o... For nonlinear systems with backlash-like hysteresis characteristics and external disturbance,a composite two-channel disturbance estimation adaptive controller is proposed to improve the trajectory tracking accuracy of the system.The unmodeled hysteresis and external disturbances are treated as lumped uncertainties,which are approximated by radial basis neural network and disturbance estimator respectively.These approximations are then linearly fused to form the compensation term for the lumped uncertainty.The second order linear filter is employed to estimate multiple differential terms,which are integrated into the controller design and dynamic system state updates,thereby reducing computational complexity.A weighted fusion mechanism is implemented for the two channels,and the adaptive update rate for each channel is determined based on the deviation between the lumped uncertainty reference value and the output of each channel.To address the challenges posed by the discontinuity of deviation and maintain system stability,the first-order low-pass filter is applied to smooth the deviation,enhancing system robustness.A trajectory tracking simulation of a single-input single-output nonlinear system is conducted to compare the performance of the proposed controller with baseline controllers,demonstrating the effectiveness of the composite two-channel disturbance estimation adaptive controller. 展开更多
关键词 composite adaptive control disturbance estimator external disturbance hysteretic nonlinearity neural network
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X-ray pulsar-based airborne clock error estimation using Gaussian process 认领 引用
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作者 Yusong WANG Yidi WANG +4 位作者 Kun JIANG Wenhai JIAO Minzhang SONG Liansheng LI Wei ZHENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期541-550,共10页
In order to autonomously calibrate the airborne clocks of deep space explorers,this paper proposes an X-ray pulsar-based airborne clock error estimation method.A pulse phase propagation model incorporating the clock e... In order to autonomously calibrate the airborne clocks of deep space explorers,this paper proposes an X-ray pulsar-based airborne clock error estimation method.A pulse phase propagation model incorporating the clock error is derived.Given that both the clock noise and the pulsar timing noise are of power-law spectral densities,their combination is modeled as a Fractional Brownian Motion(FBM)with a fractional-order power spectral density.The clock error series is modeled as a Gaussian Process(GP)with a mean function in the form of 2-order polynomial and an FBM-based covariance function.Finally,the clock error and the hyperparameters of GP are fast estimated by an iterated estimation method.The proposed method is validated via the real clock error data of the G05 satellite in the Global Positioning System(GPS)and the real data of pulsars from the Neutron star Interior Composition ExploreR(NICER). 展开更多
关键词 Atomic clock Clock error estimation Fractional Brownian motion Gaussian process X-ray pulsar
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State estimation improvement in strongly coupled data assimilation with a two-scale Lorenz model 认领 引用
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作者 Yanqiu Gao Shaoting Ge +2 位作者 Jicai Zhang Yiting Wang Qun He 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2026年第1期176-189,共14页
Coupled data assimilation(CDA)is a powerful strategy for integrating observations with coupled numerical models.This strategy holds great potential for enhancing weather and climate reanalysis and prediction.How to ad... Coupled data assimilation(CDA)is a powerful strategy for integrating observations with coupled numerical models.This strategy holds great potential for enhancing weather and climate reanalysis and prediction.How to address crossscale interactions in CDA is an important issue.In particular,the cross-scale interactions in the strongly coupled data assimilation(SCDA)framework pose substantial challenges.In this study,increasing the state estimation accuracy using an ensemble adjustment Kalman filter based on the two-scale Lorenz’96(tsL96)model is investigated.Using the SCDA framework,we adopt cross-component localization factors and several covariance inflation schemes to address the filter divergence problem.The results show that ensembles of an appropriate size can achieve good assimilation results,the optimal localization parameters are scale-dependent for the model variables,and the adaptive inflation scheme outperforms the static fixed and relaxation-to-prior spread schemes.Although these experiments were carried out using an ideal framework,this study provides a valuable reference for improving estimation accuracy with the SCDA framework in operational simulation and prediction models. 展开更多
关键词 state estimation ensemble adjustment Kalman filter strongly coupled data assimilation multiple-scale
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An adaptive active vibration suppression method for diverse wind tunnel aircraft models 认领 引用
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作者 Mengde ZHOU Chenjin SUN +4 位作者 Qi ZHAO Binkai ZHU Wei WU Yuhang REN Wei LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期370-384,共15页
Under the condition of frequent replacement of wind tunnel models,multiple types of wind tunnel models are fixed by a slender support sting with low stiffness damping.When excited by wind load,various models produce r... Under the condition of frequent replacement of wind tunnel models,multiple types of wind tunnel models are fixed by a slender support sting with low stiffness damping.When excited by wind load,various models produce random multi-dimensional vibration with different characteristics,which makes it impossible to obtain accurate and efficient aerodynamic data.Therefore,in order to ensure the reliable and efficient conduction of wind tunnel test,a wind-tunnel-modeladaptive vibration control method is proposed in this paper.First,the split type adaptive vibration suppression structure is designed.Second,the multi-dimensional vibration characteristic characterization method is derived and the vibration characteristic identification method of the system is designed.Then,a vibration state estimation model is established according to the identification results of vibration characteristics,and a multi-actuator cooperative control method based on vibration state estimation is constructed.Finally,a model-adaptive vibration control system is built,and vibration characteristics identification and hammer experiments are carried out for two types of typical models.The results show that the proposed model-adaptive vibration control method increases the equivalent damping ratio of pitch and yaw dimensions of the high-aspect-ratio class model by 8.19 times and 48.81 times,respectively.The equivalent damping ratio of pitch and yaw dimensions of the highslenderness-ratio class model is increased by 16.44 and 5.43 times,respectively.It provides a strong guarantee for the reliable and efficient development of multi-type wind tunnel test tasks. 展开更多
关键词 Active damping Model support system Vibration characteristic identification Vibration control Vibration state estimation Wind tunnels
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